{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 04 创建 ``numpy.array``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ``numpy.array``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr = np.array([i for i in range(10)])\n",
    "nparr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nparr.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 其他创建 ``numpy.array`` 的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ``zeros``"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建一维0数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10, dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(shape=(3, 5), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(shape=(5,3),dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建全\"1\"矩阵和创建全\"N\"矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(shape=(3,5),dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ones' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-a0fa796ecd66>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mones\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'ones' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,5)).reshape(5,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-92d9801631a7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m666\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.full(shape=(3, 5), fill_value=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,5),666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full(fill_value=666, shape=(3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666],\n",
       "       [666, 666, 666, 666, 666]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=np.full(fill_value=666, shape=(3, 5))\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[666, 666, 666, 666, 666],\n",
       " [666, 666, 666, 666, 666],\n",
       " [666, 666, 666, 666, 666]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([666, 666, 666, 666, 666]),\n",
       " array([666, 666, 666, 666, 666]),\n",
       " array([666, 666, 666, 666, 666])]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "full 默认的数据类型是整形"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### arange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "生成等步长数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(0, 20, 2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 20, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-be482ad462d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "[i for i in range(0, 1, 0.2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.2, 0.4, 0.6, 0.8])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 1, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(0, 10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(10)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### linspace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在0到20之间直接生成10个等步长的点，包括0和20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  2.22222222,  4.44444444,  6.66666667,  8.88888889,\n",
       "       11.11111111, 13.33333333, 15.55555556, 17.77777778, 20.        ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 20, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.  ,  0.25,  0.5 ,  0.75,  1.  ])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 1, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 随机整数randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10)    # [0, 10)之间的随机数 不包括10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 6, 1, 8, 1, 6, 8, 0, 1, 4])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 9, 9, 5, 2, 3, 3, 2, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 5, 3, 8, 5],\n",
       "       [2, 7, 9, 6, 0],\n",
       "       [0, 9, 9, 9, 7]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, size=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 8, 3, 7, 2],\n",
       "       [9, 9, 2, 4, 4],\n",
       "       [1, 5, 1, 7, 7]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(10, size=(3,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### seed\n",
    "制定随机种子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 6, 9, 4, 3],\n",
       "       [1, 0, 8, 7, 5],\n",
       "       [2, 5, 5, 4, 8]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0, 10, size=(3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 6, 9, 4, 3],\n",
       "       [1, 0, 8, 7, 5],\n",
       "       [2, 5, 5, 4, 8]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.random.randint(0, 10, size=(3,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(666)\n",
    "np.linspace(0,1,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 默认生成0-1均匀分布的浮点数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3209399001674066"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.8578588 ,  0.76741234,  0.95323137,  0.29097383,  0.84778197],\n",
       "       [ 0.3497619 ,  0.92389692,  0.29489453,  0.52438061,  0.94253896],\n",
       "       [ 0.07473949,  0.27646251,  0.4675855 ,  0.31581532,  0.39016259]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### normal 正态分布"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4695214344146364"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158.26630268638127"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(10, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.39169545,  0.45389374,  0.74236332, -0.49769162,  1.74136436],\n",
       "       [ 1.5963624 ,  0.37360023,  0.61788591,  0.07740808, -1.53327804],\n",
       "       [-0.30423301, -1.25313284,  0.37453502,  0.64378283,  2.70243069]])"
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    "np.random.normal(0, 1, (3, 5))"
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   "source": [
    "`np.random.<TAB>` 查看random中的更多方法"
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    "np.random.normal?"
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     "text": [
      "Help on built-in function normal:\n",
      "\n",
      "normal(...) method of mtrand.RandomState instance\n",
      "    normal(loc=0.0, scale=1.0, size=None)\n",
      "    \n",
      "    Draw random samples from a normal (Gaussian) distribution.\n",
      "    \n",
      "    The probability density function of the normal distribution, first\n",
      "    derived by De Moivre and 200 years later by both Gauss and Laplace\n",
      "    independently [2]_, is often called the bell curve because of\n",
      "    its characteristic shape (see the example below).\n",
      "    \n",
      "    The normal distributions occurs often in nature.  For example, it\n",
      "    describes the commonly occurring distribution of samples influenced\n",
      "    by a large number of tiny, random disturbances, each with its own\n",
      "    unique distribution [2]_.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    loc : float or array_like of floats\n",
      "        Mean (\"centre\") of the distribution.\n",
      "    scale : float or array_like of floats\n",
      "        Standard deviation (spread or \"width\") of the distribution.\n",
      "    size : int or tuple of ints, optional\n",
      "        Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
      "        ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
      "        a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
      "        Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray or scalar\n",
      "        Drawn samples from the parameterized normal distribution.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scipy.stats.norm : probability density function, distribution or\n",
      "        cumulative density function, etc.\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    The probability density for the Gaussian distribution is\n",
      "    \n",
      "    .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n",
      "                     e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n",
      "    \n",
      "    where :math:`\\mu` is the mean and :math:`\\sigma` the standard\n",
      "    deviation. The square of the standard deviation, :math:`\\sigma^2`,\n",
      "    is called the variance.\n",
      "    \n",
      "    The function has its peak at the mean, and its \"spread\" increases with\n",
      "    the standard deviation (the function reaches 0.607 times its maximum at\n",
      "    :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n",
      "    `numpy.random.normal` is more likely to return samples lying close to\n",
      "    the mean, rather than those far away.\n",
      "    \n",
      "    References\n",
      "    ----------\n",
      "    .. [1] Wikipedia, \"Normal distribution\",\n",
      "           http://en.wikipedia.org/wiki/Normal_distribution\n",
      "    .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability,\n",
      "           Random Variables and Random Signal Principles\", 4th ed., 2001,\n",
      "           pp. 51, 51, 125.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    Draw samples from the distribution:\n",
      "    \n",
      "    >>> mu, sigma = 0, 0.1 # mean and standard deviation\n",
      "    >>> s = np.random.normal(mu, sigma, 1000)\n",
      "    \n",
      "    Verify the mean and the variance:\n",
      "    \n",
      "    >>> abs(mu - np.mean(s)) < 0.01\n",
      "    True\n",
      "    \n",
      "    >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n",
      "    True\n",
      "    \n",
      "    Display the histogram of the samples, along with\n",
      "    the probability density function:\n",
      "    \n",
      "    >>> import matplotlib.pyplot as plt\n",
      "    >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n",
      "    >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n",
      "    ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
      "    ...          linewidth=2, color='r')\n",
      "    >>> plt.show()\n",
      "\n"
     ]
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   "source": [
    "help(np.random.normal)"
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